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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Evaluating methods to reduce duration of near-threshold fatigue crack growth rate measurements for low-alloy steels in hydrogen gas

Measurement of the near-threshold fatigue crack growth rate (da/dN) vs. stress-intensity factor range (∆K) relationship in hydrogen gas is essential for maximizing the calculated design fatigue life of high-pressure hydrogen storage vessels. However, such measurements are rarely performed, since the low cyclic loading frequencies applied in standard practice lead to prohibitively protracted test durations. The objective of this study was to demonstrate two means for reducing test durations when measuring near-threshold da/dN vs. ΔK relationships under decreasing ΔK for low-alloy pressure vessel steels in hydrogen gas: 1) imposing steeper K-gradients relative to the recommended limits in standards such as ASTM E647, and 2) increasing cyclic loading frequency relative to typical values applied during fatigue crack growth testing of low-alloy steels in hydrogen gas. Recognizing that steeper K-gradients could amplify loading-history effects, test methods employing this approach were designed to mitigate such effects by either maintaining constant K max or gradually increasing the K-gradient as the threshold was approached. Although the varying K-gradient method was vulnerable to loading-history effects in the form of plasticity-induced crack closure, particularly at lower stress ratio (R) and higher starting K max values, these effects could be compensated by applying the adjusted compliance ratio (ACR) method. Here, it was demonstrated that steeper K-gradients in concert with increased cyclic loading frequency reduced the duration of near-threshold fatigue crack growth tests in hydrogen gas by more than 99% relative to standard practices.

Fatigue threshold↗
DOE OSTI2025

Effects of artificial collisions, filtering, and nonlocal closure approaches on Hermite-based Vlasov–Poisson simulations

Kinetic simulations of collisionless plasmas are computationally challenging due to phase-space mixing and filamentation, resulting in fine-scale velocity structures. This study compares three methods developed to reduce artifacts related to limited velocity resolution in Hermite-based Vlasov–Poisson simulations: artificial collisions, filtering, and nonlocal closure approaches. We evaluate each method's performance in approximating the linear kinetic response function and suppressing recurrence in linear and nonlinear regimes. Numerical simulations of Landau damping demonstrate that artificial collisions, particularly higher orders of the Lenard-Bernstein collisional operator, most effectively recover the correct damping rate across a range of wavenumbers. Moreover, Hou-Li filtering and nonlocal closures underdamp high wavenumber modes in linear simulations, and the Lenard-Bernstein collisional operator overdamps low wavenumber modes in both linear and nonlinear simulations. This study demonstrates that hypercollisions offer a robust approach to kinetic simulations, accurately capturing collisionless dynamics with limited velocity resolution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Distributed and communication-efficient solutions to linear equations with special sparse structure

In this paper we report two distributed and communication-efficient algorithms based on the multi-agent system are proposed to solve a system of linear equations with the Laplacian sparse system matrix. One algorithm is based on the gradient descent method in optimization. In this algorithm, the agents only share partial information instead of all of their collective state vectors to save significant communication. The other algorithm is obtained by approximating Newton’s method for a faster convergence rate. Although it requires twice as much communication as the first one, it is still communication-efficient given the low dimension of the information shared among agents. The convergence at a linear rate is proved for both algorithms, and a comprehensive comparison of their convergence rate, communication burden, and computation costs is also performed. The proposed algorithms can be applied to various systems to solve those problems that can be modeled as a system of linear equations with a Laplacian sparse system matrix. Simulation results with the electric power system illustrate their effectiveness.

42 ENGINEERING↗
DOE OSTI2021

An adaptive Hessian approximated stochastic gradient MCMC method

Bayesian approaches have been successfully integrated into training deep neural networks. One popular family is stochastic gradient Markov chain Monte Carlo methods (SG-MCMC), which have gained increasing interest due to their ability to handle large datasets and the potential to avoid overfitting. Although standard SG-MCMC methods have shown great performance in a variety of problems, they may be inefficient when the random variables in the target posterior densities have scale differences or are highly correlated. Here, we present an adaptive Hessian approximated stochastic gradient MCMC method to incorporate local geometric information while sampling from the posterior. The idea is to apply stochastic approximation (SA) to sequentially update a preconditioning matrix at each iteration. The preconditioner possesses second-order information and can guide the random walk of a sampler efficiently. Instead of computing and saving the full Hessian of the log posterior, we use limited memory of the samples and their stochastic gradients to approximate the inverse Hessian-vector multiplication in the updating formula. Moreover, by smoothly optimizing the preconditioning matrix via SA, our proposed algorithm can asymptotically converge to the target distribution with a controllable bias under mild conditions. To reduce the training and testing computational burden, we adopt a magnitude-based weight pruning method to enforce the sparsity of the network. Our method is user-friendly and demonstrates better learning results compared to standard SG-MCMC updating rules. The approximation of inverse Hessian alleviates storage and computational complexities for large dimensional models. Numerical experiments are performed on several problems, including sampling from 2D correlated distribution, synthetic regression problems, and learning the numerical solutions of heterogeneous elliptic PDE. The numerical results demonstrate great improvement in both the convergence rate and accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Influence of Single-Crystalline Morphology on the Electrochemical Behavior of High-Nickel Layered Oxide Cathodes

High-Nickel layered oxides are being pursued for their high specific capacities. With increasing nickel content, the layered oxides also suffer from structural and morphological instabilities, which degrade the electrochemical performance. Single-crystalline morphology presents an attractive solution to these problems, simultaneously reducing surface area available for parasitic reactions and preventing electrolyte penetration into the bulk with the removal of grain boundaries. Single-crystal cathodes have already shown promising results with NMC composition cathodes, but there are still gaps in the fundamental understanding of how single-crystalline morphology alters electrochemical behavior. We attempt to fill some of these gaps by studying in detail the electrochemical operation of polycrystalline and single-crystalline LiNiO 2 (LNO). Single-crystal LNO is prepared through a molten salt method yielding high-quality, distinct crystals with comparable lattice chemistry to polycrystalline LNO. The single-crystal LNO achieves far greater stability during long-term cycling, reaching 500 cycles with 82.5% capacity retention. Interestingly, the single-crystalline LNO also displays superior rate performance, delivering 157 mA h g −1 at 10C discharge rate. Investigation of the phase evolution behavior during cycling strongly suggests that the absence of grain boundaries in single-crystalline LNO is responsible for the superior performance.

25 ENERGY STORAGE↗
DOE OSTI2023

DUNE-PRISM – A New Method to Measure Neutrino Oscillations

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long baseline neutrino oscillation experiment designed to make precision measurements in a 1.2 2.4 MW neutrino beam, which is directed 1285 km from the Fermi National Accelerator Laboratory (Fermilab) to the Sandford Underground Research Facility (SURF) in South Dakota. Neutrinos are measured at two detector facilities: a near detector located at Fermilab close where the beam is produced and a far detector at SURF. The neutrino beam can be configured to be composed primarily of either muon or anti-muon neutrinos. DUNE measures the disappearance of muon and anti-muon neutrinos and appearance electron and anti-electron neutrinos in the neutrino beam. Measuring these neutrino flavour transitions provides DUNE with sensitivity to the neutrino mass ordering, $\delta_{CP}$, $\theta_{13}$, $\theta_{23}$ and the magnitude of $\Delta m^2_{23}$. The DUNE Precision Reaction Independent Spectrum Measurement (DUNE-PRISM) concept presents a novel way to perform a neutrino oscillation analysis, which has the potential to significantly reduce the impact of large systematic uncertainties in the neutrino interaction model. The PRISM method linearly combines measurements of off-axis neutrino interactions at the DUNE near detector to produce data-driven predictions of the oscillated neutrino event rate spectrum at the far detector. By building an oscillated far detector prediction directly from data, any unknown or poorly modelled neutrino interaction effects will be naturally incorporated into the measurement of the parameters of the neutrino oscillation model. This thesis presents the first complete neutrino oscillation analysis for DUNE using the PRISM method. Details of the methodology are fully explained and the prospects for further improvements to the techniques described are highlighted. The expected impact and relative importance of the neutrino flux, cross section and detector systematic uncertainties are described in detail. Finally, this thesis demonstrates that the PRISM method is capable of performing a measurement of the oscillation parameters that is robust against neutrino interaction modelling errors.

Hasnip, Ciaran↗
DOE OSTI2018

Quantitative Mapping of Nanoscale Chemical Dynamics in Sub-sampled Operando (S)TEM Images Using Spatio-Temporal Analytics

One of the main limitations in the use of operando scanning transmission electron microscopes to study dynamic chemical processes is the effect of the electron beam on the kinetics of the reaction being observed. Here we demonstrate that a flexible Gaussian mixture model can be used to extract quantitative information directly from sub-sampled images, i.e.~images where not all the pixels in the image are illuminated with the beam. The use of this method is demonstrated for the charge/discharge cycle of a Li-battery, where the deposition and dissolution of Li at the anode can be accurately quantified for sub-sampling levels down to 1%. Performing operando imaging using a small fraction of the pixels means that the observations can significantly reduce the effect of the beam, automatically increase the imaging speed and decrease the total data transfer rate required. Such new software capabilities offer the potential to significantly widen the application of operando hardware approaches to study nanoscale dynamics in materials.

Stanfill, Bryan A.↗
DOE OSTI2019

Endovascular Stroke Therapy with a Novel 6-French Aspiration Catheter

Background and Purpose: Direct thrombus aspiration is increasingly used as a first-line therapy in acute ischemic stroke with large vessel occlusion. We assessed the performance and safety of a novel aspiration catheter available: the 6-French AXS Catalyst catheter. Materials and Methods: We conducted a cohort study from a prospective clinical registry of consecutive stroke patients treated by mechanical thrombectomy between March 2016 and July 2016. Baseline clinical and imaging characteristics, recanalization rates, complications, and clinical outcomes were analyzed. Results: Among the 60 patients included, 30 were treated using aspiration alone, 14 were treated using aspiration and then stent retriever as a rescue therapy, and 16 were treated using aspiration combined with a stent retriever straightaway. Successful recanalization (mTICI2b/3) was achieved in 85% patients and functional independence in 48.3%. We observed one intracranial perforation and one vertebral artery dissection. Symptomatic intracranial hemorrhage occurred in 5% and mortality in 21.7%. Conclusion: Endovascular stroke therapy using the AXS Catalyst catheter seems safe and effective, with similar performance than other reperfusion catheters.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Estimating the joint distribution of rate parameters across multiple reactions in the absence of experimental data

Here, a procedure for determining the joint uncertainty of Arrhenius parameters across multiple combustion reactions of interest is demonstrated. This approach is capable of constructing the joint distribution of the Arrhenius parameters arising from the uncertain measurements performed in specific target experiments without having direct access to the underlying experimental data. The method involves constructing an ensemble of hypothetical data sets with summary statistics consistent with the available information reported by the experimentalists, followed by a fitting procedure that learns the structure of the joint parameter density across reactions using this consistent hypothetical data as evidence. The procedure is formalized in a Bayesian statistical framework, employing maximum-entropy and approximate Bayesian computation methods and utilizing efficient Markov chain Monte Carlo techniques to explore data and parameter spaces in a nested algorithm. We demonstrate the application of the method in the context of experiments designed to measure the rates of selected chain reactions in the H 2 -O 2 system and highlight the utility of this approach for revealing the critical correlations between the parameters within a single reaction and across reactions, as well as for maximizing consistency when utilizing rate parameter information in predictive combustion modeling of systems of interest.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation

Predicting and understanding the chemical bond is one of the major challenges of computational quantum chemistry. Kohn–Sham density functional theory (KS-DFT) is the most common method, but approximate density functionals may not be able to describe systems where multiple electronic configurations are equally important. Multiconfigurational wave functions, on the other hand, can provide a detailed understanding of the electronic structures and chemical bonds of such systems. In the complete active space self-consistent field (CASSCF) method, one performs a full configuration interaction calculation in an active space consisting of active electrons and active orbitals. However, CASSCF and its variants require the selection of these active spaces. This choice is not black box; it requires significant experience and testing by the user, and thus active space methods are not considered particularly user-friendly and are employed only by a minority of quantum chemists. Our goal is to popularize these methods by making it easier to make good active space choices. Here, we present a machine learning protocol that performs an automated selection of active spaces for chemical bond dissociation calculations of main group diatomic molecules. The protocol shows high prediction performance for a given target system as long as a properly correlated system is chosen for training. Good active spaces are correctly predicted with a considerably better success rate than random guess (larger than 80% precision for most systems studied). Our automated machine learning protocol shows that a “black-box” mode is possible for facilitating and accelerating the large-scale calculations on multireference systems where single-reference methods such as KS-DFT cannot be applied.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Gamma-ray spectroscopic characterization of long, rapidly-grown KSr 2 I 5 :Eu crystals

Collimation and volumetric studies were done to evaluate the gamma-ray spectroscopic performance of KSr 2 I 5 :Eu scintillators grown via the vertical Bridgman method. To demonstrate practical crystal growth for large-scale production, Ø1 in. × 6 in. long crystals were grown using fast pulling rates up to 5 mm/h and short cooling times. The long crystals were sectioned into shorter Ø1 in. cylinders to investigate sample-to-sample variation of scintillation properties, the effects of self-absorption and scintillation non-uniformity using different gamma-ray excitation configurations. The best light output and energy resolution achieved for the largest volume and aspect ratio crystals (60 cm 3 Ø1 in x 4.7 in. long) were 53,500 ph/MeV and 4.6% at 662 keV. Finally, we produced eight Ø1 in. × 1 in. (12.5 cm 3 ) cylindrical samples, four from each boule, with energy resolutions between 3.1%-3.5% at 662 keV.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Accelerating FEM-Based Corrosion Predictions Using Machine Learning

Atmospheric corrosion of metallic parts is a widespread materials degradation phenomena that is challenging to predict given its dependence on many factors (e.g. environmental, physiochemical, and part geometry). For materials with long expected service lives, accurately predicting the degree to which corrosion will degrade part performance is especially difficult due to the stochastic nature of corrosion damage spread across years or decades of service. The Finite Element Method (FEM) is a computational technique capable of providing accurate estimates of corrosion rate by numerically solving complex differential Eqs. characterizing this phenomena. Nevertheless, given the iterative nature of FEM and the computational expense required to solve these complex equations, FEM is ill-equipped for an efficient exploration of the design space to identify factors that accelerate or deter corrosion, despite its accuracy. In this work, a machine learning based surrogate model capable of providing accurate predictions of corrosion with significant computational savings is introduced. Specifically, this work leverages AdaBoosted Decision trees to provide an accurate estimate of corrosion current per width given different values of temperature, water layer thickness, molarity of the solution, and the length of the cathode for a galvanic couple of aluminum and stainless steel.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Noble Gas Management: SBMOF 1 vs. NUCON Carbon

Pacific Northwest National Laboratory (PNNL), in collaboration with Flibe Energy, Inc., demonstrated the fabrication of metal organic frameworks (MOFs) into engineered beads to manage off-gases released from molten salt reactors. Among the many MOF options, SBMOF 1 was selected as an ideal sorbent to manage off-gases released from a molten salt reactor because its pore size is optimal. The SBMOF 1 was synthesized and fabricated into engineered particles using three different methods, with and without polymers. All the engineered particles were exposed to Co-60 radiation (1,000 kGy) to demonstrate their radiation stability. Single column breakthrough experiments were performed at different temperatures, activation conditions, flow rates, and off-gas compositions to optimize the Xe capacity at the breakthrough point and at saturation. The Xe loading in SBMOF 1 was compared with that of standard solid sorbent (Nucon carbon) under identical conditions. Based on the single column experiments, SBMOF 1 outperforms the Nucon carbon at all the temperatures and reduces the mass (to as little as 55%) of sorbent needed to achieve the same performance as carbon. The bed volume is also reduced, down to 48% of that of Nucon carbon, when the MOF is used. These results clearly demonstrate improvement in the amount of adsorbent needed and show a reduction in bed volume compared to traditional sorbent material.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Three-flavour neutrino oscillations with MINOS and CHIPS

MINOS was a long-baseline neutrino oscillation experiment comprising two functionally identical detectors that observed Fermilab's NuMI neutrino beam in its low energy tune, at distances of 1 km and 735 km. When NuMI switched to a higher-power medium energy tune in 2012, the MINOS detectors continued to operate as MINOS+. Since its commissioning in 2003, the MINOS Far Detector has also been able to detect atmospheric neutrinos. Atmospheric neutrino oscillations are sensitive to the mass splitting m2 32 and mixing angle 23, and are also subject to the Matter E ect as the neutrinos pass through the Earth, which a ects neutrinos and antineutrinos di erently in a way that depends upon the mass hierarchy. This thesis presents the rst atmospheric neutrino analysis using data from the MINOS+ era, and the rst dedicated MINOS atmospheric neutrino analysis to use a full three- avour mixing model. It includes 10.79 kiloton years of new data and encompasses almost an entire period of the 11-year solar cycle, from 2003 to 2014. The CHIPS experiment aims to reduce construction costs of large water Cherenkov detectors to $200-300k per kiloton by submerging detectors with a lightweight structure in bodies of water on the surface of the Earth. Such detectors could reach masses of 1 Mton and would assist with the search for CP violation in the neutrino sector by measuring the rate of e appearance in a beam. A detailed reconstruction framework for CHIPS has been developed, incorporating a novel method based on the timing of PMT hits. This framework has been used to study the performance of di erent designs for a 10 kiloton CHIPS R&D module, and to demonstrate that e events can be identi ed in a sparsely-instrumented detector with a 6% coverage of 3" PMTs.

Perch, Andrew John↗
DOE OSTI2018

Self-optimized construction of transition rate matrices from accelerated atomistic simulations with Bayesian uncertainty quantification

Here, a massively parallel method to build large transition rate matrices from temperature-accelerated molecular dynamics trajectories is presented. Bayesian Markov model analysis is used to estimate the expected residence time in the known state space, providing crucial uncertainty quantification for higher-scale simulation schemes such as kinetic Monte Carlo or cluster dynamics. The estimators are additionally used to optimize where exploration is performed and the degree of temperature acceleration on the fly, giving an autonomous, optimal procedure to explore the state space of complex systems. The method is tested against exactly solvable models and used to explore the dynamics of C15 interstitial defects in iron. Our uncertainty quantification scheme allows for accurate modeling of the evolution of these defects over timescales of several seconds.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Wearable radio-frequency sensing of respiratory rate, respiratory volume, and heart rate

Abstract Many health diagnostic systems demand noninvasive sensing of respiratory rate, respiratory volume, and heart rate with high user comfort. Previous methods often require multiple sensors, including skin-touch electrodes, tension belts, or nearby off-the-body readers, and hence are uncomfortable or inconvenient. This paper presents an over-clothing wearable radio-frequency sensor study, conducted on 20 healthy participants (14 females) performing voluntary breathing exercises in various postures. Two prototype sensors were placed on the participants, one close to the heart and the other below the xiphoid process to couple to the motion from heart, lungs and diaphragm, by the near-field coherent sensing principle. We can achieve a satisfactory correlation of our sensor with the reference devices for the three vital signs: heart rate ( r = 0.95), respiratory rate ( r = 0.93) and respiratory volume ( r = 0.84). We also detected voluntary breath-hold periods with an accuracy of 96%. Further, the participants performed a breathing exercise by contracting abdomen inwards while holding breath, leading to paradoxical outward thorax motion under the isovolumetric condition, which was detected with an accuracy of 83%.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Age and Method of Release Affect Migratory Performance of Hatchery Steelhead

Abstract Hatchery programs that are designed to aid recovery of natural populations of anadromous salmonids, including steelhead Oncorhynchus mykiss, require locally derived, natural-origin broodstock. In such programs, achieving smoltification size thresholds may require extending hatchery rearing beyond age 1. We compared out-migration survival and travel rates of 142,990 PIT-tagged steelhead smolts released at age 1 (S1 rearing strategy) or age 2 (S2 rearing strategy) over five release years at Winthrop National Fish Hatchery (WNFH, Okanogan County, Washington). An S2 rearing cycle produced larger smolts with more uniform size distributions, resulting in higher survival during the first portion of their out-migration than for S1 smolts in three of the five release years. The S2 smolts migrated more rapidly to the ocean than S1 smolts in all years except 2011 and arrived in the Columbia River estuary 5.4 d earlier on average than the S1 smolts. The S1 steelhead that did not leave during the volitional release were subsequently forced from the hatchery to measure their survival. Nonvolitional S1 migrants were smaller and had survival rates that were 2.3–66.3 times lower than those of S1 steelhead that left WNFH on their own. The same was true for S2 steelhead, but the magnitude of the survival difference between volitional migrants and fish forced from the raceways was less variable and ranged from 2.5- to 4.6-fold. We conclude that S2 rearing can be a successful strategy for producing smolts from local natural-origin broodstock, with out-migration survival and travel times that are equivalent to or better than those of S1 smolts produced from nonlocal broodstock. Received July 25, 2016; accepted March 31, 2017 Published online May 31, 2017

Tatara, Christopher P.↗
DOE OSTI2019

Kinetics of Recycling CF/EP Composites by Thermal Excitation of Cr{sub 2}O{sub 3}

Recycling high performance carbon fibers (CFs) by thermal excitation of Cr{sub 2}O{sub 3} semiconductor was proposed. The kinetic model of decomposition of epoxy resin was established and the method for solving kinetic parameters was proposed. The influences of temperature and treatment time on the decomposition rate of epoxy resin were investigated. The kinetic equation was established and the accuracy was verified experimentally. The morphology of the recycled CF was observed and monofilament tensile strength was determined. Results indicated that temperature and treatment time had positive correlation with decomposition rate of the epoxy resin. The reaction order n was 1.5, with excitation energy E of 85.03 kJ·mol{sup −1} and pre-exponential factor k{sub 0} of 1.26 × 10{sup 6} min{sup −1}. The established kinetic equation could be used to estimate the temperature and treatment time. Prolongation of treatment time could improve the monofilament tensile strength. The monofilament tensile strength of the recycled CF at 480 °C for 30 min was 102.72% of that of the original CF, indicating that surface heat treatment was performed on CF during the recycling process.

36 MATERIALS SCIENCE↗